🏥 Life (and) science
In his popular column
for Science Translational Medicine, Derek Lowe rightly pressed AI developers to “attack the right problems” if they want to have an impact on drug discovery. In short, this means pointing AI to the later stages of drug discovery. Why? This is where potential improvements have the most impact based on the cost of capital, expected return from a new drug, patent lifetimes, etc. However, most computational drug discovery companies pitch “No more stumbling around screening piles of molecules! No more tedious property optimization!”, Lowe writes, but “the real problem is having drug candidates fail in the clinic. All that other stuff is a roundoff error compared to the clinical failure rate.” He wraps by concluding, “what everyone wants are AI systems, computational techniques, and models that will reduce all that finger-crossing and tachycardia, but that’s, unfortunately, some ways off.” For the record, I’m with you, Derek.
I also think we have a compelling answer to talk about. I’m hugely excited to publicly discuss our investment in Vienna-based Allcyte, a “startup that has found a way to uncover bespoke cancer therapies”, as per Jeremy Kahn’s story in Fortune
. Today, drugs are moved into clinical trials because they exhibit favorable activity in preclinical systems such as animal models and cell lines. However, humans are not mice, and cell lines don’t recapitulate the complexity of human cancer and its (immune) microenvironment. This is where Allcyte steps in. The company makes use of high-content microscopy and computer vision to interpret the activity of cancer drugs directly in viable, primary cancer tissue from the patient at the single-cell level. In doing so, Allcyte moves beyond genomic testing of cancer patients to identify drugs that could work - an approach that sees 90% of lung cancer patients not respond to suggested therapies - and into functional precision medicine at the cellular level. This isn’t just research. At the American Society of Hematology’s annual conference in December 2020, Allcyte showed results from a clinical study of oncologists who used its technology on 143 late-stage blood cancer patients who exhausted all known treatment options. Allcyte’s system could find a drug amongst 136 candidates that lead to longer patient survival in 55% of the 56 cases in which patients were treatable. This is a major win for pharma and patients. Indeed, Allcyte’s technology is already in use with 10 major pharma companies to help them build confidence in advancing drugs into clinical trials only after having validated that the drug works in what is a “mini clinical study” on primary patient tissues.
The Broad Institute, a powerhouse of computational biology, received
a $150M gift from Eric and Wendy Schmidt to significantly expand its work in AI-first biology. We can expect this to drive even more R&D, open-sourcing, and company creation. Recall that Recursion, which filed
to go public on the NASDAQ this month, making use
of the Broad’s CellProfiler at the very beginning of its life to showcase how cellular phenotypes can be efficiently interpreted with computer vision.
joined Natural Cycles as the two FDA cleared digital solutions for birth control. These products produce predictions on the chances of pregnancy using either the start date of their period (Clue) or daily temperature measurements (Natural Cycles). As its user inputs more cycle data, the product can narrow the risk window down to two weeks or less.
🌎 The (geo)politics of AI
Last month’s newsletter looked at the UK’s AI Roadmap and evidence for Chinese funding of defense-related AI research at publicly-funded UK institutions. Over in the US, the National Security Commission - a bipartisan commission led by Google’s Eric Schmidt and former US Deputy Secretary of Defense Robert Work - released its final report
on how the US can “win the AI era”. It presents a strategy for the US to defend against AI threats, employ AI for national security in a responsible manner, and “win the broader technology competition for the sake of our prosperity, security, and welfare.” The authors are clear about what is at stake:
“For the first time since World War II, America’s technological predominance—the backbone of its economic and military power—is under threat. China possesses the might, talent, and ambition to surpass the United States as the world’s leader in AI in the next decade if current trends do not change. Simultaneously, AI is deepening the threat posed by cyber-attacks and disinformation campaigns that Russia, China, and others are using to infiltrate our society, steal our data, and interfere in our democracy. The limited uses of AI-enabled attacks to date represent the tip of the iceberg. Meanwhile, global crises exemplified by the COVID-19 pandemic and climate change highlight the need to expand our conception of national security and find innovative AI-enabled solutions.”
Their recommendation is to drive for widespread integration of AI in the workforce (civilian and government) and the military by 2025. This includes a US Digital Service Academy to train talent for government and a push for Congress to pass a National Defense Education Act II to invest in fellowships across the higher education stack.
On the funding front, the government should double non-defense funding for AI R&D to $32B per year by 2026. It should triple the number of National AI Research Institutes, develop a National AI Research Infrastructure for cloud computing and training data, and reform IP policies to favor patenting of AI in the US.
The US is also called on to shore up resources for designing and fabricating microelectronics after almost entirely outsourcing these capabilities to Asia in the last decades. It must also modernize export controls and screen foreign investment to protect US enterprises. The report puts it clearly: “the U.S. supply chain for advanced chips is at risk without concerted government action. Rebuilding domestic chip manufacturing will be expensive, but the time to act is now.”
On the defense front, The Pentagon must drive organizational reforms, develop new warfighting concepts and weapons, and work with the DoD to augment and focus its AI R&D portfolio. Relatedly, President Biden passed an executive order for a 100-day review
of supply chains for semiconductors, large-capacity batteries for EVs, pharmaceuticals, and rare-earth metals. The outcome of this review could add more urgency to infrastructure spending for supply chain onshoring. All the while, the US is treading a careful path of overhauling its military-industrial complex while hopefully avoiding
a more expansive military-civil fusion that we see in China.
Over in the UK, Boris Johnson has said
that the £16.5B defense budget boost announced last year would be used to fund a new military research center for AI. The government will invest in AI technologies, unmanned aircraft, directed energy weapons, and other battlefield use cases. Various British and international startups are involved in this program, such as Improbable Defence, Adarga, and Rebellion Defence.
Sam Altman wrote a long piece entitled Moore’s Law for Everything
in which he paints a future for the American economy when we reach AGI. He argues that this revolution will create phenomenal wealth because the price of most (if not all) labor will fall to zero. This wealth, however, needs to be distributed widely to enable more people to participate and to collectively raise the standard of living. To do so, he makes the case for an American Equity Fund, which I think is a good one. The idea is that because machines will drive labor, costs will fall precipitously. So rather than taxing labor, we should be taxing capital. For everyone to participate in the accrual of the value of companies that produce learning machines, the American Equity Fund would tax companies above a certain valuation in the form of shares. All adult citizens would receive an annual distribution from the American Equity Fund and would be free to spend it however they wish. I think we could further incentivize long-term ownership by implementing a capital gains rate that decays with the holding period. I also think that a national Equity Fund like this one could rebalance the feelings that big tech is bad and takes advantage of consumers. If consumers earn equity (not discount vouchers!) in the companies they transact with regularly, I think we’d have much more alignment.
Apple made a big move by announcing
a €1B investment into the Munich area to develop a European Silicon Design Center. It will encompass a 30,000 square meter facility for 1,500 engineers to focus on power management design, application processors, and wireless technologies including 5G. The statement deepens Apple’s commitment to Germany where in 2015 it opened its Bavarian Design Center for 350 engineers. This facility created custom silicon to deliver improved performance and power efficiency for several Apple products including the iPhone, iPad, and M1-based Mac. By 2019, Apple opened more silicon engineering sites in Germany and a new radio technology site in Austria.
Meanwhile, Taiwan is suffering the worst drought
in 56 years, forcing the restricted use of water. In addition to the ecological challenges this poses, Taiwan’s semiconductor industry will also take a hit because it relies on massive amounts
of water. TSMC needs 156,000 tons of water per day, which is ⅓ of all water used in Taiwan’s key science parks. The company does reuse almost 90% of its water.
Taiwan has also accused
Bitmain, one of China’s biggest crypto computing infrastructure companies, of illegally running two research centers in Taiwan and hiring employees from TSMC. Upon registration, these Chinese companies do not disclose their activities in chip design or research.
Intel’s CEO made a splash
by stating the company would double down on domestic semiconductor manufacturing by investing $20B to build two new chip fabs in Arizona. “Intel is back. The old Intel is now the new Intel”, he said. This temporarily shot TSMC’s share price down by 10%. But money is one thing - TSMC remains the dominant player technology-wise: the company captures
almost 90% of the $21B in 2020 revenue for the most advanced 10nm-5nm processes. Indeed, recall that Apple ditched Intel chips with their own line of processors based on the Arm core.
Graphcore pushed major updates
to their Poplar software stack that enables the scale-out capabilities of the IPU compute system.
Facebook Reality Labs (and the CTRL-Labs team) demonstrated
wristbands that use electromyography to translate neural signals from muscles into actions while also providing haptic feedback. These wristbands will be compatible with VR experiences too.
In China, there may be a hint of popular pushback
against facial recognition. A state-controlled broadcaster, China Central Television (CCTV, no pun intended) ran a 10-minute investigative segment where undercover reporters talked to surveillance camera makers who showed how cameras could “recognize and document a person’s age, ethnicity, and even emotional state. The cameras also successfully identified return customers, allowing sellers to call up purchase histories in real-time.” A draft law could pass in 2021 that will let the government regulate how facial recognition is used in the commercial domain. In particular, it suggests that personal identification shall only operate in “public venues” for the purposes of public security.
🏭 Big tech
In Japan, Honda launched
their first Level-3 ADAS vehicle, which is able to pass slow-moving vehicles without driver intervention. Similarly, Volvo’s 550-person self-driving unit called Zenseact and LiDAR-maker Luminar also announced
a highway autopilot system that will be offered to third parties and on specific Volvo cars. Luminar is also providing
its sensor to SAIC Motor, one of China’s largest automakers, and will open a Shanghai office.
In the US, several states have passed rulings to allow
delivery robots such as Starship to operate on sidewalks. These include Pennsylvania, Virginia, Idaho, Florida, and Wisconsin.
that 9 months after launching their API, more than 300 applications are using GPT-3. The API service generates 4.5 billion words per day on average.
using facial recognition and biometric tracking using Netradyne cameras for their 75,000 delivery drivers in the United States. This is sparking controversy because drivers do not have a choice but to opt-in if they wish to keep their employment.